Yanshuang Hao

University of Science and Technology Beijing

Papers

Total Citations

H-Index

About

Yanshuang Hao is a researcher whose work lies at the intersection of robotics, visual navigation, and parallel image processing. Her key contributions focus on leveraging cellular neural networks (CNN) to accelerate visual information processing for mobile robots—a critical bottleneck in real-time robotic navigation. In her most cited work, Hao proposed a fast algorithm that exploits the inherent parallel processing capability of CNNs, enabling rapid image analysis that directly improves the quality and speed of visual navigation systems. This foundational paper has garnered 3 citations, establishing her early impact in the field. Hao’s research addresses a fundamental challenge in robotics: how to process complex visual data quickly enough for autonomous decision-making. By integrating CNN-based architectures into mobile robot platforms, she has helped bridge the gap between theoretical neural network models and practical robotic applications. Her work is particularly relevant for students and researchers interested in real-time computer vision, embedded systems, and bio-inspired computing. Hao’s contributions continue to inform the development of faster, more efficient visual navigation systems for autonomous robots.